Point‐convolution‐based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple‐input multiple‐output radar

Abstract Compared with traditional approaches that used vision sensors which can provide a high‐resolution representation of targets, millimetre‐wave radar is robust to scene lighting and weather conditions, and has more applications. Current methods of human skeletal pose estimation can reconstruct targets, but they lose the spatial information or don't take the density of point cloud into consideration. We propose a skeletal pose estimation method that combines point convolution to extract features from the point cloud. By extracting the local information and density of each point in the point cloud of the target, the spatial location and structure information of the target can be obtained, and the accuracy of the pose estimation is increased. The extraction of point cloud features is based on point‐by‐point convolution, that is, different weights are applied to different features of each point, which also increases the nonlinear expression ability of the model. Experiments show that the proposed approach is effective. We offer more distinct skeletal joints and a lower mean absolute error, average localisation errors of 6.1 cm in X, 3.5 cm in Y and 3.3 cm in Z, respectively

Detection of Subsurface Target Based on FDA-MIMO Radar

As a new type of radar, the FDA-MIMO radar has a good improvement on side lobe suppression and target detection performance compared with the conventional MIMO radar. However, the existing researches on FDA-MIMO radar are almost based on far-field. In this paper, FDA-MIMO radar is applied to the detection of subsurface targets. Aimed at near-subsurface targets, we formulated the signal model of FDA-MIMO radar and combined it with the algorithm of grid of beam (GOB) to detect. Compared with conventional MIMO radar detection, we verified the effectiveness of the proposed method through theoretical simulation